# -*- coding: UTF-8 -*-
import numpy as np
from os import listdir


#函数说明：itemgetter根据某个或某几个字典字段排序列表
def itemgetter(*items):
	if len(items) == 1:
		item = items[0]

		def g(obj):
			return obj[item]
	else:
		def g(obj):
			return tuple(obj[item] for item in items)
	return g

#函数说明:kNN算法,分类器
def classify0(inX, dataSet, labels, k):
	dataSetSize = dataSet.shape[0]
	diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet
	sqDiffMat = diffMat**2
	sqDistances = sqDiffMat.sum(axis=1)
	distances = sqDistances**0.5
	sortedDistIndices = distances.argsort()
	classCount = {}
	for i in range(k):
		voteIlabel = labels[sortedDistIndices[i]]
		classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
	sortedClassCount = sorted(classCount.items(),key=itemgetter(1),reverse=True)
	return sortedClassCount[0][0]


#函数说明:将32x32的二进制图像转换为1x1024向量。
def img2vector(filename):
	returnVect = np.zeros((1, 1024))
	fr = open(filename)
	for i in range(32):
		lineStr = fr.readline()
		for j in range(32):
			returnVect[0, 32*i+j] = int(lineStr[j])
	return returnVect

#函数说明:手写数字分类测试
def handwritingClassTest():
	hwLabels = []
	trainingFileList = listdir('trainingDigits')
	m = len(trainingFileList)
	trainingMat = np.zeros((m, 1024))
	for i in range(m):
		fileNameStr = trainingFileList[i]
		classNumber = int(fileNameStr.split('_')[0])
		hwLabels.append(classNumber)
		trainingMat[i,:] = img2vector('trainingDigits/%s' % (fileNameStr))
	testFileList = listdir('testDigits')
	errorCount = 0.0
	mTest = len(testFileList)
	for i in range(mTest):
		fileNameStr = testFileList[i]
		classNumber = int(fileNameStr.split('_')[0])
		vectorUnderTest = img2vector('testDigits/%s' % (fileNameStr))
		classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
		print("分类返回结果为%d\t真实结果为%d" % (classifierResult, classNumber))
		if(classifierResult != classNumber):
			errorCount += 1.0
	print("总共错了%d个数据\n错误率为%f%%" % (errorCount, errorCount/mTest))




#main函数
if __name__ == '__main__':
	handwritingClassTest()
